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Julia Computing raises $24M Series A

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Re: Julia Computing raises $24M Series A

#61

Post by tylermw »

notafraudster wrote: post #30
leephillips wrote: post #15

Earlier quoted context omitted.

If you are doing very high-performance numerical work, your choices¹ are Fortran, C, C++, or Julia. Julia is way more fun to program in than the other choices. Also, it has some properties² that make code re-use and re-combination especially easy. 1 https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-cl... 2 https://arstechnica.com/science/2020/10/the-unreasonable-eff...

What's the argument against using R and dropping into RCpp for very limited tasks? I (helped) write a very widely used R modelling package and while I wasn't doing anything on the numerical side, we seemed to get great performance from this approach -- and workflow-wise it wasn't too dissimilar to 25 years ago where I had to occasionally drop in X86 assembly to speed up C code! (Not a hater of Julia at all, very much…

Not much an argument at all, if you ask me. There's definitely a benefit to only having to learn a single language (rather than R and C++), but the library/package ecosystem in R is hard to beat; unless you're doing truly bespoke computational work, the number of mature statistical libraries/packages in R is unmatched. Rcpp's syntactic sugar means most slow R bottlenecks can be written in C++ almost verboten, but without the interpreted performance penalty. One of R's best and under-emphasized features is its straightforward foreign-function interface: it's easy to creating bindings to C/C++/Fortran routines (and Rust support is coming along as well).

I've been impressed with Julia, but it's hard to beat 25 years of package development.

Re: Julia Computing raises $24M Series A

#62

Post by ziotom78 »

djhaskin987 wrote: post #2

Can someone please explain to me, a mere mortal, what is the big deal with Julia. Why use it, when there are so many other good languages out there with more community/support? Honest question.

I am using Julia extensively since 2013, and I can say that it's awesome! But don't try to use it if you're looking for a general-purpose scripting language: Python is far better suited for this. Similarly, if you want to produce standalone executables, C++, Rust, Go or Nim are better.

However, Julia is perfect if you write mathematical/physical/engineering simulations and data analysis codes, which is my typical use case. Its support for multiple dispatch and custom operators lets you to write very efficient code without sacrificing readability, which is a big plus. Support for HPC computing is very good too.

Re: Julia Computing raises $24M Series A

#63

Post by hawk_ »

KenoFischer wrote: post #47
hawk_ wrote: post #41

it's not obvious to me what's their revenue model?

Nothing complicated. Stream 1: Build amazing products for particular domains, charge license fees Stream 2: Build a great SaaS platform for running Julia, charge for compute Since all of our domain products are built in Julia and often involve significant compute cost for their intended application, hopefully both at the same time :).

thanks for the answer Keno. i guess an example Stream 1 product is Pumas. i didn't realize it's a separate product from Julia. my background is in finance and i am curious if you have any plans to break into that domain (examples on your website include julia language use)

Re: Julia Computing raises $24M Series A

#64

Post by ska »

devoutsalsa wrote: post #45
leephillips wrote: post #15

Earlier quoted context omitted.

If you are doing very high-performance numerical work, your choices¹ are Fortran, C, C++, or Julia. Julia is way more fun to program in than the other choices. Also, it has some properties² that make code re-use and re-combination especially easy. 1 https://www.hpcwire.com/off-the-wire/julia-joins-petaflop-cl... 2 https://arstechnica.com/science/2020/10/the-unreasonable-eff...

Doesn’t Python offer this speed in it’s scientific libraries, too? Or is the answer “yes, if you use the libraries are written in Fortran, C, C++, or Julia!”?

> Or is the answer “yes, if you use the libraries are written in Fortran, C, C++, or Julia!”?

That's basically the answer.

Re: Julia Computing raises $24M Series A

#65

Post by ViralBShah »

codekilla wrote: post #24
ChrisRackauckas wrote: post #17

Earlier quoted context omitted.

Julia Computing is not a services company. There are commercial products built off of this stack which are the core of Julia Computing. For example, https://pumas.ai/ is a product for pharmacology modeling and simulation, and runs on the JuliaHub cloud platform of Julia Computing. It is already a big deal in the industry, with the quote everyone refers to "Pumas has emerged as our 'go-to' tool for most of our analyse…

Wanted to ask if JuliaDB is something that might get more development attention? Or will that remain a community project? (I see it’s been in need of a release for awhile.)

In general, the community has discussed reviving the project (or at least the ideas and some of its codebase). Julia computing will also be contributing as part of that revival.

Re: Julia Computing raises $24M Series A

#67

Post by devoutsalsa »

spenczar5 wrote: post #54
leephillips wrote: post #48

Earlier quoted context omitted.

When those libraries are fast, it is because they are using Numpy routines written in Fortran or C. And you can get a lot done with those libraries, of course. But they’re only fast if your code can be fit into stereotyped vector patterns. As soon as you need to write a loop, you get slow Python performance. Python + Scipy would not be a good choice for writing an ocean circulation or galaxy merger simulation. EDIT:…

I work in research software for astronomy, and I cannot agree with that. A very large amount of astronomy software is in Python. Numba has gone a long way toward making non-vectorized array operations very fast from Python. Most people use a ton of numpy and scipy. It turns out that phrasing things as array operations with numpy operators is quite natural in this field, including for things like galaxy merger simulat…

Out of curiosity, how does someone get into the work you’re doing? Do you just kind of fall into it accidentally? Get a PhD in astronomical computing (if that’s a thing)?

Re: Julia Computing raises $24M Series A

#68

Post by systemvoltage »

awaythrowact wrote: post #39

Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…

Great summary. I've worked with scientists that love Julia and more power to them. As a software engineer, there are still rough edges in productionizing Julia (yes, I know there are a few examples of large scale production code). As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python. Having used Julia for last 4 years and having to maintain that code in production environment, I am strongly convinced that Julia has a niche but it is not going to be a contestant to Python/Java/C++ depending on the use case. Which really is a shame - I want one goddamn language to rule them all. I want that and tried to give a fair chance to Julia.

Re: Julia Computing raises $24M Series A

#69

Post by maxnoe »

spenczar5 wrote: post #54
leephillips wrote: post #48

Earlier quoted context omitted.

When those libraries are fast, it is because they are using Numpy routines written in Fortran or C. And you can get a lot done with those libraries, of course. But they’re only fast if your code can be fit into stereotyped vector patterns. As soon as you need to write a loop, you get slow Python performance. Python + Scipy would not be a good choice for writing an ocean circulation or galaxy merger simulation. EDIT:…

I work in research software for astronomy, and I cannot agree with that. A very large amount of astronomy software is in Python. Numba has gone a long way toward making non-vectorized array operations very fast from Python. Most people use a ton of numpy and scipy. It turns out that phrasing things as array operations with numpy operators is quite natural in this field, including for things like galaxy merger simulat…

Numba essentially does the same as julia, compile to llvm bytecode, in julia, that's a language design decision, in python it is a library.

You can get very far with these approaches I python, but having these at the language level just has more potential for optimization and less friction.

The debugability of numba code is very limited and code coverage does ot work at all.

Having a high level language that has scientific use at its core is just great.

Python has the maturity and community size on its side, but Jul is catching up on that quickly.

Re: Julia Computing raises $24M Series A

#70

Post by slownews45 »

KenoFischer wrote: post #47
hawk_ wrote: post #41

it's not obvious to me what's their revenue model?

Nothing complicated. Stream 1: Build amazing products for particular domains, charge license fees Stream 2: Build a great SaaS platform for running Julia, charge for compute Since all of our domain products are built in Julia and often involve significant compute cost for their intended application, hopefully both at the same time :).

My own view is that Julia Computing is distinguished a bit by a more product focus.

It's a bit less of a pure language / infra play and more a product play. Ie, docker / containers was almost a pure infra play in the end. These guys make actual things you can use.

The later sells better into business I think and is less likely to be competed against. Google / AWS et al are generally pretty quick to compete on the infra play level.

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